Equalizers are an important element in many diverse digital information applications, such as voice, data, and video communications. These applications employ a variety of transmission media. Although the various media have differing transmission characteristics, none of them is perfect. That is, every medium induces variation into the transmitted signal, such as frequency-dependent phase and amplitude distortion, multipath reception, other kinds of ghosting, such as voice echoes, and Rayleigh fading. In addition to channel distortion, virtually every sort of transmission also suffers from noise, such as additive white gausian noise (“AWGN”). Equalizers are therefore used as acoustic echo cancelers (for example in full-duplex speakerphones), video deghosters (for example in digital television or digital cable transmissions), signal conditioners for wireless modems and telephony, and other such applications.
Those skilled in the art will recognize that prior art equalizers have difficulty coping with ghosts having a signal strength close to that of the main signal. Typically ghosts are caused by multipathing—that is, portions of the transmitted signal that are reflected by one or more terrain features to arrive at the receiver by less direct paths. Consequently, ghosts are typically weaker, and arrive after, the main signal. However, in certain environments, especially downtown areas, which have numerous large buildings that can completely mask a signal, signal strength can be highly directional. A receiver positioned in the shadow of a tall building, for example, might not receive any direct signal, but still receive strong signals that are reflected off of one or more other buildings. Thus, in this environment, ghosts that are as strong as the “main” signal are possible. Furthermore, since the strength of the signal may be controlled as much by the albedo, size, or shape of the reflective surface as by the number of reflections in the path, ghosts that arrive before the strongest signal are far more likely.
Similar problems occur in systems that use multiple transmitters in order to provide the widest possible coverage for a digital transmission. Multiple transmitters would permit a wider area to be covered using less total broadcast power, and could help to fill in dark areas where the transmission from one transmitter may be blocked. Thus, using multiple transmitters can provide wider and more complete coverage for virtually any digital transmission. However, using multiple transmitters creates a serious problem when the receiver is at a “seam” between two transmitters, because the additional signal can appear as a “ghost” that can be as large as the “main” signal.
Those skilled in the art will appreciate that existing receiver technology handles ghosts by filtering them out in order to interpret the “main” signal. But in a multi-transmitter environment, or an area which generates multiple reflections and highly directional signals, this strategy is unworkable. It makes little sense to design a system to filter out a ghost that can be an arbitrarily large fraction of the “main” signal's size. Near the margins the best this subtractive strategy can ever provide is a signal strength equal to the strongest single echo—the energy from the secondary signals, whether from reflections or additional transmitters, is wasted.
In short, in a multi-transmitter or downtown environments the “main” signal becomes a meaningless concept. In order to operate efficiently in such a multi-signal environment, a digital receiver must operate with a different paradigm. What is needed is a digital receiver that employs an additive strategy—that is, one in which the energy from one or more relatively large ghosts can be captured and used to aid in the synchronization process, rather than filtered out and discarded. Such a receiver could both function with ghosts 100% of the size of the “main” signal, and provides substantially superior performance whenever ghosts exceed about 70% of the size of the “main” signal.
FIG. 1 illustrates a block diagram of a typical digital communication receiver, including channel coding and equalization, indicated generally at 100. The receiver 100 comprises a demodulation and sync component 110, which converts the received analog signal back into a digital format. The receiver 100 further comprises an equalizer 120, an inner decoder 130, a de-interleaver 140, and an outer decoder 150. The inner coding is typically convolutional coding, while the outer coding is typically block coding, most often Reed-Solomon coding. The convolutional and block coding are generally combined in order to exploit the complementary advantages of each.
FIG. 2 is a diagram of an equalizer 120 such as is commonly used in the digital receiver 100 shown in FIG. 1. Typically, the equalizer 120 includes a controller 228, a finite impulse response (“FIR”) filter 222, a decision device 226, and a decision feedback equalizer (“DFE”) 224. The FIR filter 222 receives the input signal 221. The FIR filter 222 is used to cancel pre-ghosts—that is, ghost signals that arrive before the main transmission signal. The decision device 226 examines its inputs and makes a decision as to which one of the received signals at its input is the signal to be transmitted to the output 229. The input to the decision device 226 is modified by a decision feedback equalizer 224, which is used to cancel post-ghosts—that is, ghost signals that arrive after the main transmission signal—and the residual signal generated by the FIR filter 222.
The decision device 226 is typically a hard decision device, such as a slicer. For example, in an 8VSB system, the slicer can be a decision device based upon the received signal magnitude, with decision values of 0, ±2, ±4, and ±6, in order to sort the input into symbols corresponding to the normalized signal values of ±1, ±3, ±5, and ±7. For another example, the slicer can be multi-dimensional, such as those used in quadrature amplitude modulation (“QAM”) systems.
The controller 228 receives the input data and the output data and generates filter coefficients for both the FIR filter 222 and the decision feedback filter 224. Those skilled in the art will appreciate that there are numerous methods suitable for generating these coefficients, including LMS and RLS algorithms.
FIG. 4 is a graph of signal magnitude versus time illustrating a post ghost having a magnitude 100% of the “main” signal. The main transmission signal M is illustrated at a relative magnitude of 1 (0 dB). After a time delay of D, a 100% post-ghost signal G arrives. In this situation, the prior art equalizer 120 of FIG. 2 has difficulty selecting the “main” signal for the proper output 229—since the very concept of a “main” signal is meaningless with a 100% ghost. If the ghost signal G is treated as a pre-ghost, then the FIR filter 222 will have tap values equal to 1 and will go infinite. If the ghost signal G is treated as a post-ghost, then the error of the feedback filter 224 will be magnified and the filter becomes unstable. Further, if the ghost G changes magnitude due to its phase variation or channel variation, the main signal M and the ghost signal G can exchange roles (based upon maximum magnitude).
Therefore, what is needed is an equalizer that is better adapted to cope with ghosts having an arbitrarily large magnitude relative to the main signal, including the possibility of a “ghost” having a magnitude that can temporarily exceed the magnitude of the “main” signal. The present invention is directed towards meeting these needs, as well as providing other advantages over prior equalizers.